In the Python ecosystem, there are many tools dealing with source code: linters, test coverage collection systems, and many others. Many of them use special comments, and as a rule, the style of these comments is very similar.
But you know what? There is no single standard for such comments. Seriously.
The internal implementation of reading such comments is also different. Someone uses regular expressions, someone uses even more primitive string processing tools, and someone uses full-fledged parsers, including the Python parser or even written from scratch.
This is exactly the problem that this library solves. It describes a simple and intuitive standard for action comments, and also offers a ready-made parser that creators of other tools can use.
Looks interesting - is there a PEP [0] for this?
A formally verified system is easier for the model to check and consequently easier for it to program to. I suppose the question is whether or not formal methods are sufficiently tractable that they actually do help the LLM be able to finish the job before it runs out of its context.
Regardless, I often use coding assistants in that manner:
1. First, I use the assistant to come up with the success condition program
2. Then I use the assistant to solve the original problem by asking it to check with the success condition program
3. Then I check the solution myself
It's not rocket science, and is just the same approach we've always taken to problem-solving, but it is nice that modern tools can also work in this way. With this, I can usually use Opus or GPT-5.2 in unattended mode.
0: https://wiki.roshangeorge.dev/w/Blog/2025-12-11/LLMs_Excel_A...
> 2. Then I use the assistant to solve the original problem by asking it to check with the success condition program
This sounds a lot like Test-Driven Development. :)